code: def viz_classification_preds(probs, y_test):
      """look at prediction breakdown"""
      plt.subplot(121)
      plt.hist(probs[:, 1][y_test == 0], label="Class 0")
      plt.hist(probs[:, 1][y_test == 1], label="Class 1", alpha=0.8)
      plt.ylabel("Count")
      plt.xlabel("Predicted probability of class 1")
      plt.legend()
  
      plt.subplot(122)
      preds = np.argmax(probs, axis=1)
      plt.title("ROC curve")
      fpr, tpr, thresholds = metrics.roc_curve(y_test, preds)
      plt.xlabel("False positive rate")
      plt.ylabel("True positive rate")
      plt.plot(fpr, tpr)
      plt.tight_layout()
      plt.show()
  
file_path: imodels/notebooks/demo_helper.py
imports: import numpy
  from scipy.io.arff import loadarff
  from sklearn import metrics
  import matplotlib.pyplot
  from sklearn.datasets import fetch_openml
  from sklearn.model_selection import train_test_split
  import os
label_desc: Use image node_id 1 for calling a global function with example usage: viz_classification_preds(probs, y_test) without return types